{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 题目\n",
    "某餐饮企业的增长陷入迟滞，同时不同菜品口味和不同经营模式的餐馆更是层出不穷。在内忧外患的情况下，该餐饮企业希望结合餐饮行业现状，分析客户和订单的数据，挖掘数据中的信息，通过对客户流失进行预测寻找到相应对策，从而提高利润。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导出依赖库\n",
    "导入pandas、matplotlib、seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据\n",
    "读取user_loss.csv与info_new .csv，存储为users和info，打印数据的维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "客户信息表的维度：\n",
      " (2431, 36)\n",
      "订单详情表的维度：\n",
      " (6611, 21)\n"
     ]
    }
   ],
   "source": [
    "users = pd.read_csv(\"../data/user_loss.csv\", encoding='gbk')\n",
    "info = pd.read_csv(\"../data/info_new .csv\", encoding='utf-8')\n",
    "print(\"客户信息表的维度：\\n\", users.shape)\n",
    "print(\"订单详情表的维度：\\n\", info.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里可以看出数据中有2431条用户信息；6611条订单信息 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 浏览客户信息表信息\n",
    "使用info()方法查看两个数据的信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2431 entries, 0 to 2430\n",
      "Data columns (total 36 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   USER_ID         2431 non-null   int64  \n",
      " 1   MYID            0 non-null      float64\n",
      " 2   ACCOUNT         2431 non-null   object \n",
      " 3   NAME            2431 non-null   object \n",
      " 4   ORGANIZE_ID     2431 non-null   int64  \n",
      " 5   ORGANIZE_NAME   2431 non-null   object \n",
      " 6   DUTY_ID         0 non-null      float64\n",
      " 7   TITLE_ID        0 non-null      float64\n",
      " 8   PASSWORD        2431 non-null   object \n",
      " 9   EMAIL           0 non-null      float64\n",
      " 10  LANG            0 non-null      float64\n",
      " 11  THEME           0 non-null      float64\n",
      " 12  FIRST_VISIT     310 non-null    object \n",
      " 13  PREVIOUS_VISIT  0 non-null      float64\n",
      " 14  LAST_VISITS     289 non-null    object \n",
      " 15  LOGIN_COUNT     0 non-null      float64\n",
      " 16  ISEMPLOYEE      0 non-null      float64\n",
      " 17  STATUS          0 non-null      float64\n",
      " 18  IP              0 non-null      float64\n",
      " 19  DESCRIPTION     0 non-null      float64\n",
      " 20  QUESTION_ID     0 non-null      float64\n",
      " 21  ANSWER          0 non-null      float64\n",
      " 22  ISONLINE        0 non-null      float64\n",
      " 23  CREATED         2431 non-null   object \n",
      " 24  LASTMOD         0 non-null      float64\n",
      " 25  CREATER         2045 non-null   float64\n",
      " 26  MODIFYER        0 non-null      float64\n",
      " 27  TEL             2431 non-null   int64  \n",
      " 28  STUNO           2431 non-null   int64  \n",
      " 29  QQ              0 non-null      float64\n",
      " 30  WEIXIN          0 non-null      float64\n",
      " 31  SEX             2431 non-null   object \n",
      " 32  POO             2429 non-null   object \n",
      " 33  ADDRESS         2429 non-null   object \n",
      " 34  AGE             2431 non-null   int64  \n",
      " 35  TYPE            2431 non-null   object \n",
      "dtypes: float64(20), int64(5), object(11)\n",
      "memory usage: 683.8+ KB\n"
     ]
    }
   ],
   "source": [
    "users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6611 entries, 0 to 6610\n",
      "Data columns (total 21 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   info_id             6611 non-null   int64  \n",
      " 1   emp_id              6611 non-null   int64  \n",
      " 2   number_consumers    6611 non-null   int64  \n",
      " 3   mode                0 non-null      float64\n",
      " 4   dining_table_id     6611 non-null   int64  \n",
      " 5   dining_table_name   6611 non-null   int64  \n",
      " 6   expenditure         6611 non-null   int64  \n",
      " 7   dishes_count        6611 non-null   int64  \n",
      " 8   accounts_payable    6611 non-null   int64  \n",
      " 9   use_start_time      6611 non-null   object \n",
      " 10  check_closed        0 non-null      float64\n",
      " 11  lock_time           6611 non-null   object \n",
      " 12  cashier_id          0 non-null      float64\n",
      " 13  pc_id               0 non-null      float64\n",
      " 14  order_number        0 non-null      float64\n",
      " 15  org_id              6611 non-null   int64  \n",
      " 16  print_doc_bill_num  0 non-null      float64\n",
      " 17  lock_table_info     0 non-null      float64\n",
      " 18  order_status        6611 non-null   int64  \n",
      " 19  phone               6611 non-null   int64  \n",
      " 20  name                6611 non-null   object \n",
      "dtypes: float64(7), int64(11), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "info.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将时间转换为时间格式\n",
    "将users数据的'CREATED'列转换为时间格式;\n",
    "将info数据的'use_start_time'和'ock_time'转换为时间格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2015/8/17 20:09\n",
       "1       2014/10/31 12:06\n",
       "2         2014/6/3 12:19\n",
       "3        2015/4/25 19:03\n",
       "4        2015/11/8 12:34\n",
       "              ...       \n",
       "2426      2014/8/9 20:32\n",
       "2427     2014/9/17 19:36\n",
       "2428     2014/2/21 12:05\n",
       "2429     2015/4/21 11:08\n",
       "2430     2014/7/27 13:45\n",
       "Name: CREATED, Length: 2431, dtype: object"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['CREATED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "users['CREATED'] = pd.to_datetime(users['CREATED'])\n",
    "info['use_start_time'] = pd.to_datetime(info['use_start_time'])\n",
    "info['lock_time'] = pd.to_datetime(info['lock_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2015-08-17 20:09:00\n",
       "1      2014-10-31 12:06:00\n",
       "2      2014-06-03 12:19:00\n",
       "3      2015-04-25 19:03:00\n",
       "4      2015-11-08 12:34:00\n",
       "               ...        \n",
       "2426   2014-08-09 20:32:00\n",
       "2427   2014-09-17 19:36:00\n",
       "2428   2014-02-21 12:05:00\n",
       "2429   2015-04-21 11:08:00\n",
       "2430   2014-07-27 13:45:00\n",
       "Name: CREATED, Length: 2431, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['CREATED']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 客户流失与年龄的关系\n",
    "以年龄为横坐标，客户流失人数为纵坐标，以users的'TYPE'列为区分，分别绘制客户流失与年龄之间的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGE\n",
       "20     7\n",
       "21    24\n",
       "22    17\n",
       "23    13\n",
       "24    15\n",
       "25    11\n",
       "26    22\n",
       "27    18\n",
       "28    11\n",
       "29    20\n",
       "30    14\n",
       "31    14\n",
       "32    14\n",
       "33    16\n",
       "34    19\n",
       "35    14\n",
       "36    18\n",
       "37    11\n",
       "38    16\n",
       "39    20\n",
       "40    19\n",
       "41    14\n",
       "42    14\n",
       "43     7\n",
       "44    14\n",
       "45    16\n",
       "46     9\n",
       "47    13\n",
       "48    10\n",
       "49    13\n",
       "50     9\n",
       "51     1\n",
       "52     3\n",
       "53     3\n",
       "54     2\n",
       "55     1\n",
       "56     2\n",
       "57     4\n",
       "58     3\n",
       "59     6\n",
       "60     5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = users.loc[users['TYPE'] == '已流失', ['AGE', 'TYPE']]['AGE'].value_counts().sort_index()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGE\n",
       "20    11\n",
       "21    29\n",
       "22    32\n",
       "23    25\n",
       "24    20\n",
       "25    33\n",
       "26    19\n",
       "27    34\n",
       "28    30\n",
       "29    29\n",
       "30    26\n",
       "31    25\n",
       "32    25\n",
       "33    33\n",
       "34    21\n",
       "35    26\n",
       "36    26\n",
       "37    31\n",
       "38    30\n",
       "39    30\n",
       "40    28\n",
       "41    14\n",
       "42    13\n",
       "43    17\n",
       "44    20\n",
       "45    20\n",
       "46    20\n",
       "47    20\n",
       "48    14\n",
       "49    17\n",
       "50    20\n",
       "51    11\n",
       "52     2\n",
       "53     8\n",
       "54     3\n",
       "55     8\n",
       "56     8\n",
       "57     8\n",
       "58    10\n",
       "59     6\n",
       "60     2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = users.loc[users['TYPE'] == '非流失', ['AGE', 'TYPE']]['AGE'].value_counts().sort_index()\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGE\n",
       "20    25\n",
       "21    54\n",
       "22    49\n",
       "23    48\n",
       "24    33\n",
       "25    43\n",
       "26    44\n",
       "27    40\n",
       "28    43\n",
       "29    36\n",
       "30    41\n",
       "31    37\n",
       "32    37\n",
       "33    37\n",
       "34    34\n",
       "35    36\n",
       "36    34\n",
       "37    51\n",
       "38    38\n",
       "39    42\n",
       "40    30\n",
       "41    19\n",
       "42    23\n",
       "43    22\n",
       "44    29\n",
       "45    22\n",
       "46    28\n",
       "47    27\n",
       "48    26\n",
       "49    23\n",
       "50    22\n",
       "51    10\n",
       "52    11\n",
       "53     8\n",
       "54     9\n",
       "55     5\n",
       "56     5\n",
       "57     4\n",
       "58     6\n",
       "59     8\n",
       "60     6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = users.loc[users['TYPE'] == \"准流失\", ['AGE', 'TYPE']]['AGE'].value_counts().sort_index()\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>已流失</th>\n",
       "      <th>未流失</th>\n",
       "      <th>准流失</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    已流失  未流失  准流失\n",
       "20    7   11   25\n",
       "21   24   29   54\n",
       "22   17   32   49\n",
       "23   13   25   48\n",
       "24   15   20   33\n",
       "25   11   33   43\n",
       "26   22   19   44\n",
       "27   18   34   40\n",
       "28   11   30   43\n",
       "29   20   29   36\n",
       "30   14   26   41\n",
       "31   14   25   37\n",
       "32   14   25   37\n",
       "33   16   33   37\n",
       "34   19   21   34\n",
       "35   14   26   36\n",
       "36   18   26   34\n",
       "37   11   31   51\n",
       "38   16   30   38\n",
       "39   20   30   42\n",
       "40   19   28   30\n",
       "41   14   14   19\n",
       "42   14   13   23\n",
       "43    7   17   22\n",
       "44   14   20   29\n",
       "45   16   20   22\n",
       "46    9   20   28\n",
       "47   13   20   27\n",
       "48   10   14   26\n",
       "49   13   17   23\n",
       "50    9   20   22\n",
       "51    1   11   10\n",
       "52    3    2   11\n",
       "53    3    8    8\n",
       "54    2    3    9\n",
       "55    1    8    5\n",
       "56    2    8    5\n",
       "57    4    8    4\n",
       "58    3   10    6\n",
       "59    6    6    8\n",
       "60    5    2    6"
      ]
     },
     "execution_count": 410,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'已流失': a.values, '未流失': b.values, '准流失': c.values}, index=range(20, 61, 1))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '年龄与客户流失人数的关系')"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 20154 (\\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 24180 (\\N{CJK UNIFIED IDEOGRAPH-5E74}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 40836 (\\N{CJK UNIFIED IDEOGRAPH-9F84}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 19982 (\\N{CJK UNIFIED IDEOGRAPH-4E0E}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 23458 (\\N{CJK UNIFIED IDEOGRAPH-5BA2}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 25143 (\\N{CJK UNIFIED IDEOGRAPH-6237}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 20851 (\\N{CJK UNIFIED IDEOGRAPH-5173}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 31995 (\\N{CJK UNIFIED IDEOGRAPH-7CFB}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 23681 (\\N{CJK UNIFIED IDEOGRAPH-5C81}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 24050 (\\N{CJK UNIFIED IDEOGRAPH-5DF2}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 26410 (\\N{CJK UNIFIED IDEOGRAPH-672A}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20154 (\\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24180 (\\N{CJK UNIFIED IDEOGRAPH-5E74}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 40836 (\\N{CJK UNIFIED IDEOGRAPH-9F84}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 19982 (\\N{CJK UNIFIED IDEOGRAPH-4E0E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 23458 (\\N{CJK UNIFIED IDEOGRAPH-5BA2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25143 (\\N{CJK UNIFIED IDEOGRAPH-6237}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30340 (\\N{CJK UNIFIED IDEOGRAPH-7684}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20851 (\\N{CJK UNIFIED IDEOGRAPH-5173}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 31995 (\\N{CJK UNIFIED IDEOGRAPH-7CFB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 23681 (\\N{CJK UNIFIED IDEOGRAPH-5C81}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24050 (\\N{CJK UNIFIED IDEOGRAPH-5DF2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26410 (\\N{CJK UNIFIED IDEOGRAPH-672A}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.lineplot(data=df)\n",
    "plt.xlabel(\"年龄（岁）\")\n",
    "plt.ylabel(\"人数\")\n",
    "plt.title(\"年龄与客户流失人数的关系\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计男性和女性客户的流失数量\n",
    "组合1行2列统计图，绘制男性客户流失柱状图和女性客户流失柱状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[660],\n",
       "       [452],\n",
       "       [278]])"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计男性客户中几种类别的数量\n",
    "count1 = users.loc[users['SEX']==\"男\"]['TYPE'].value_counts()\n",
    "count1 = pd.DataFrame(count1)\n",
    "count1.columns = [\"数量（人）\"]\n",
    "count1\n",
    "count1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数量（人）</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TYPE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>准流失</th>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>非流失</th>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已流失</th>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      数量（人）\n",
       "TYPE       \n",
       "准流失     485\n",
       "非流失     352\n",
       "已流失     204"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计女性客户中几种类别的数量\n",
    "count2 = users[users['SEX']=='女']['TYPE'].value_counts()\n",
    "count2 = pd.DataFrame(count2)\n",
    "count2.columns=[\"数量（人）\"]\n",
    "count2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['准流失', '非流失', '已流失'], dtype='object', name='TYPE')"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index1 = count1.index\n",
    "index1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['准流失', '非流失', '已流失'], dtype='object', name='TYPE')"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index2 = count2.index\n",
    "index2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': '男性'}, xlabel='TYPE', ylabel='数量（人）'>"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax=axes[0]\n",
    "ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女性')"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 20154 (\\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 30007 (\\N{CJK UNIFIED IDEOGRAPH-7537}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 24615 (\\N{CJK UNIFIED IDEOGRAPH-6027}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 38750 (\\N{CJK UNIFIED IDEOGRAPH-975E}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 24050 (\\N{CJK UNIFIED IDEOGRAPH-5DF2}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/events.py:89: UserWarning: Glyph 22899 (\\N{CJK UNIFIED IDEOGRAPH-5973}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25968 (\\N{CJK UNIFIED IDEOGRAPH-6570}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 37327 (\\N{CJK UNIFIED IDEOGRAPH-91CF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65288 (\\N{FULLWIDTH LEFT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20154 (\\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 65289 (\\N{FULLWIDTH RIGHT PARENTHESIS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30007 (\\N{CJK UNIFIED IDEOGRAPH-7537}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24615 (\\N{CJK UNIFIED IDEOGRAPH-6027}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 27969 (\\N{CJK UNIFIED IDEOGRAPH-6D41}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 38750 (\\N{CJK UNIFIED IDEOGRAPH-975E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 24050 (\\N{CJK UNIFIED IDEOGRAPH-5DF2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "/opt/miniconda3/envs/Python-data-analysis/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22899 (\\N{CJK UNIFIED IDEOGRAPH-5973}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n",
      "findfont: Generic family 'sans-serif' not found because none of the following families were found: SimHei\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "sns.barplot(x=index1, y=count1['数量（人）'], ax=axes[0])\n",
    "axes[0].set_title(\"男性\")\n",
    "sns.barplot(x=index2, y=count2['数量（人）'], ax=axes[1])\n",
    "axes[1].set_title(\"女性\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计订单详情表重复数量\n",
    "使用duplicated()方法统计订单详情表重复数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "订单详情表重复个数： 0\n"
     ]
    }
   ],
   "source": [
    "print(\"订单详情表重复个数：\", info.duplicated(subset=['name', 'use_start_time']).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去除订单详情表中的异常值\n",
    "排除同一时间桌子被不同人使用的订单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['info_id', 'emp_id', 'number_consumers', 'mode', 'dining_table_id',\n",
       "       'dining_table_name', 'expenditure', 'dishes_count', 'accounts_payable',\n",
       "       'use_start_time', 'check_closed', 'lock_time', 'cashier_id', 'pc_id',\n",
       "       'order_number', 'org_id', 'print_doc_bill_num', 'lock_table_info',\n",
       "       'order_status', 'phone', 'name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = info[info.duplicated(subset=['dining_table_id', 'use_start_time'])].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "同一时间桌子被不同人使用的订单：\n",
      "       info_id  dining_table_id      use_start_time\n",
      "2052     3392             1484 2016-03-26 21:55:00\n",
      "2140     3480             1484 2016-03-26 21:55:00\n"
     ]
    }
   ],
   "source": [
    "print('同一时间桌子被不同人使用的订单：\\n',\n",
    "     info[(info['dining_table_id'] == info.iloc[ind[1], :]['dining_table_id']) & \n",
    "     (info['use_start_time'] == info.iloc[ind[1], :]['use_start_time'])]\n",
    "     [['info_id', 'dining_table_id','use_start_time']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info_id</th>\n",
       "      <th>emp_id</th>\n",
       "      <th>number_consumers</th>\n",
       "      <th>mode</th>\n",
       "      <th>dining_table_id</th>\n",
       "      <th>dining_table_name</th>\n",
       "      <th>expenditure</th>\n",
       "      <th>dishes_count</th>\n",
       "      <th>accounts_payable</th>\n",
       "      <th>use_start_time</th>\n",
       "      <th>...</th>\n",
       "      <th>lock_time</th>\n",
       "      <th>cashier_id</th>\n",
       "      <th>pc_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>org_id</th>\n",
       "      <th>print_doc_bill_num</th>\n",
       "      <th>lock_table_info</th>\n",
       "      <th>order_status</th>\n",
       "      <th>phone</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1326</td>\n",
       "      <td>3556</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1485</td>\n",
       "      <td>1006</td>\n",
       "      <td>423</td>\n",
       "      <td>13</td>\n",
       "      <td>423</td>\n",
       "      <td>2016-02-05 19:08:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-02-05 19:15:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882708</td>\n",
       "      <td>麻庶汐</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1327</td>\n",
       "      <td>1874</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1516</td>\n",
       "      <td>1038</td>\n",
       "      <td>1101</td>\n",
       "      <td>29</td>\n",
       "      <td>1101</td>\n",
       "      <td>2016-01-04 11:51:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-04 12:09:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881026</td>\n",
       "      <td>濮明智</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1328</td>\n",
       "      <td>3484</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1504</td>\n",
       "      <td>1010</td>\n",
       "      <td>437</td>\n",
       "      <td>20</td>\n",
       "      <td>437</td>\n",
       "      <td>2016-01-29 13:31:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-29 13:37:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882636</td>\n",
       "      <td>姜萌萌</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1329</td>\n",
       "      <td>3639</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1482</td>\n",
       "      <td>1003</td>\n",
       "      <td>251</td>\n",
       "      <td>8</td>\n",
       "      <td>251</td>\n",
       "      <td>2016-01-19 12:02:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-19 12:14:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882791</td>\n",
       "      <td>封振翔</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1330</td>\n",
       "      <td>3835</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1480</td>\n",
       "      <td>1002</td>\n",
       "      <td>363</td>\n",
       "      <td>6</td>\n",
       "      <td>363</td>\n",
       "      <td>2016-07-18 12:35:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-18 12:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882987</td>\n",
       "      <td>白子晨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6606</th>\n",
       "      <td>7971</td>\n",
       "      <td>2442</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1511</td>\n",
       "      <td>1023</td>\n",
       "      <td>710</td>\n",
       "      <td>16</td>\n",
       "      <td>710</td>\n",
       "      <td>2016-07-12 11:19:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-12 11:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881594</td>\n",
       "      <td>栾丽萍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>7972</td>\n",
       "      <td>3147</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1509</td>\n",
       "      <td>1016</td>\n",
       "      <td>1318</td>\n",
       "      <td>46</td>\n",
       "      <td>1318</td>\n",
       "      <td>2016-07-21 12:50:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-21 12:58:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882299</td>\n",
       "      <td>陈仲锋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6608</th>\n",
       "      <td>7973</td>\n",
       "      <td>1293</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1482</td>\n",
       "      <td>1002</td>\n",
       "      <td>346</td>\n",
       "      <td>6</td>\n",
       "      <td>346</td>\n",
       "      <td>2016-01-20 19:15:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-20 19:28:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880492</td>\n",
       "      <td>蒲水莲</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6609</th>\n",
       "      <td>7974</td>\n",
       "      <td>1947</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1492</td>\n",
       "      <td>1015</td>\n",
       "      <td>1401</td>\n",
       "      <td>41</td>\n",
       "      <td>1401</td>\n",
       "      <td>2016-03-28 19:50:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-03-28 19:57:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881099</td>\n",
       "      <td>温伟超</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>7975</td>\n",
       "      <td>2276</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1485</td>\n",
       "      <td>1033</td>\n",
       "      <td>301</td>\n",
       "      <td>13</td>\n",
       "      <td>301</td>\n",
       "      <td>2016-06-16 12:55:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-06-16 13:14:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881428</td>\n",
       "      <td>经子颍</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6594 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      info_id  emp_id  number_consumers  mode  dining_table_id  \\\n",
       "0        1326    3556                 4   NaN             1485   \n",
       "1        1327    1874                 7   NaN             1516   \n",
       "2        1328    3484                 5   NaN             1504   \n",
       "3        1329    3639                 2   NaN             1482   \n",
       "4        1330    3835                 2   NaN             1480   \n",
       "...       ...     ...               ...   ...              ...   \n",
       "6606     7971    2442                 4   NaN             1511   \n",
       "6607     7972    3147                10   NaN             1509   \n",
       "6608     7973    1293                 2   NaN             1482   \n",
       "6609     7974    1947                 8   NaN             1492   \n",
       "6610     7975    2276                 3   NaN             1485   \n",
       "\n",
       "      dining_table_name  expenditure  dishes_count  accounts_payable  \\\n",
       "0                  1006          423            13               423   \n",
       "1                  1038         1101            29              1101   \n",
       "2                  1010          437            20               437   \n",
       "3                  1003          251             8               251   \n",
       "4                  1002          363             6               363   \n",
       "...                 ...          ...           ...               ...   \n",
       "6606               1023          710            16               710   \n",
       "6607               1016         1318            46              1318   \n",
       "6608               1002          346             6               346   \n",
       "6609               1015         1401            41              1401   \n",
       "6610               1033          301            13               301   \n",
       "\n",
       "          use_start_time  ...           lock_time cashier_id  pc_id  \\\n",
       "0    2016-02-05 19:08:00  ... 2016-02-05 19:15:00        NaN    NaN   \n",
       "1    2016-01-04 11:51:00  ... 2016-01-04 12:09:00        NaN    NaN   \n",
       "2    2016-01-29 13:31:00  ... 2016-01-29 13:37:00        NaN    NaN   \n",
       "3    2016-01-19 12:02:00  ... 2016-01-19 12:14:00        NaN    NaN   \n",
       "4    2016-07-18 12:35:00  ... 2016-07-18 12:45:00        NaN    NaN   \n",
       "...                  ...  ...                 ...        ...    ...   \n",
       "6606 2016-07-12 11:19:00  ... 2016-07-12 11:26:00        NaN    NaN   \n",
       "6607 2016-07-21 12:50:00  ... 2016-07-21 12:58:00        NaN    NaN   \n",
       "6608 2016-01-20 19:15:00  ... 2016-01-20 19:28:00        NaN    NaN   \n",
       "6609 2016-03-28 19:50:00  ... 2016-03-28 19:57:00        NaN    NaN   \n",
       "6610 2016-06-16 12:55:00  ... 2016-06-16 13:14:00        NaN    NaN   \n",
       "\n",
       "      order_number  org_id  print_doc_bill_num  lock_table_info  order_status  \\\n",
       "0              NaN     330                 NaN              NaN             1   \n",
       "1              NaN     330                 NaN              NaN             1   \n",
       "2              NaN     330                 NaN              NaN             1   \n",
       "3              NaN     330                 NaN              NaN             1   \n",
       "4              NaN     330                 NaN              NaN             1   \n",
       "...            ...     ...                 ...              ...           ...   \n",
       "6606           NaN     330                 NaN              NaN             1   \n",
       "6607           NaN     330                 NaN              NaN             1   \n",
       "6608           NaN     330                 NaN              NaN             1   \n",
       "6609           NaN     330                 NaN              NaN             1   \n",
       "6610           NaN     330                 NaN              NaN             1   \n",
       "\n",
       "            phone  name  \n",
       "0     18688882708   麻庶汐  \n",
       "1     18688881026   濮明智  \n",
       "2     18688882636   姜萌萌  \n",
       "3     18688882791   封振翔  \n",
       "4     18688882987   白子晨  \n",
       "...           ...   ...  \n",
       "6606  18688881594   栾丽萍  \n",
       "6607  18688882299   陈仲锋  \n",
       "6608  18688880492   蒲水莲  \n",
       "6609  18688881099   温伟超  \n",
       "6610  18688881428   经子颍  \n",
       "\n",
       "[6594 rows x 21 columns]"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info.drop(index=ind, inplace=True)\n",
    "info.reset_index(drop=True)\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "异常值个数： 17\n",
      "去除异常值订单详情表维数： (6594, 21)\n"
     ]
    }
   ],
   "source": [
    "print('异常值个数：', len(ind))\n",
    "print('去除异常值订单详情表维数：', info.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计缺失值数量\n",
    "1.统计客户信息表缺失值个数；2.订单详情表缺失值个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "客户信息表缺失值个数： 46158\n",
      "订单详情表缺失值个数： 50842\n"
     ]
    }
   ],
   "source": [
    "print('客户信息表缺失值个数：', info.isnull().sum().sum())\n",
    "print('订单详情表缺失值个数：', users.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合并两个表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "合并表缺失值个数：\n",
      " USER_ID               0\n",
      "LAST_VISITS         155\n",
      "TYPE                  0\n",
      "number_consumers      7\n",
      "expenditure           7\n",
      "dtype: int64\n",
      "处理缺失值数据维度：\n",
      " (6443, 5)\n"
     ]
    }
   ],
   "source": [
    "# 获取最后一次用餐时间\n",
    "for i in range(len(users)):\n",
    "    info1 = info.iloc[info[info['name'] == users.iloc[i, 2]].index.tolist(), :]\n",
    "    if sum(info['name'] == users.iloc[i, 2]) != 0:\n",
    "        users.iloc[i, 14] = max(info1['use_start_time'])\n",
    "# 获取订单状态为1的订单\n",
    "info = info.loc[info['order_status'] == 1, ['emp_id', 'number_consumers', 'expenditure']]\n",
    "info = info.rename(columns={'emp_id': 'USER_ID'})  # 修改列名\n",
    "user = users[['USER_ID', 'LAST_VISITS', 'TYPE']]\n",
    "\n",
    "# 合并两个表\n",
    "info_user = pd.merge(user, info, left_on='USER_ID', right_on='USER_ID', how='left')\n",
    "print('合并表缺失值个数：\\n', info_user.isnull().sum())\n",
    "info_user.dropna(inplace=True)\n",
    "\n",
    "info_user.to_csv('../tmp/info_user.csv', index=False, encoding='utf-8')\n",
    "\n",
    "print('处理缺失值数据维度：\\n', info_user.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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